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        "author": "Mike Richman",
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        "description": "# histlite\n\nSee documentation on [ReadTheDocs](https://histlite.readthedocs.io/en/latest/index.html).\n\n**histlite** is a histogram calculation and plotting library that tries to be\n\"lite\" on data structures but rich in statistics and visualization features.\nSo far, development has taken place during my (Mike Richman) time as a graduate\nstudent and post-doctoral researcher in the field of particle astrophysics ---\nspecifically, working with the IceCube Neutrino Observatory.  Histlite is\nintended both to facilitate high-paced exploratory data analysis as well as to\nserve as a building block for potentially very complex maximum likelihood data\nanalysis implementations.\n\nThe core design considerations are:\n\n* It must be trivial to work with and interchange between 1D, 2D, or ND histograms.\n* It should be as simple as possible to perform bin-wise arithmetic\n  operations on one or more histograms; to perform sums, integrals, etc. and\n  thus normalizations along one or more axes simultaneously; and to perform\n  spline or user-defined functional fits\n* It should be as simple as possible to achieve publication-quality plots.\n\nThe primary histogramming functionality consists of a thin wrapper around\n:meth:`numpy.histogramdd`.  Statistical tools leverage **scipy** but include\ncustom solutions for some use cases.  (Importantly, error propagation is\ncurrently handled manually but may be migrated to the **uncertainties**\npackage in the future.)  Plotting is done using **matplotlib**.\n\n\n",
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